---
license: cc-by-sa-4.0
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- text-retrieval
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- config_name: worldcuisines
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    path: worldcuisines/train-*
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---

# M4-RAG: A Massive-Scale Multilingual Multi-Cultural Multimodal RAG

[**Paper**](https://huggingface.co/papers/2512.05959) | [**Code**](https://github.com/davidanugraha/M4-RAG)

M4-RAG is a massive-scale benchmark spanning 42 languages, 56 regional dialects and registers, and 189 countries, comprising over 80,000 culturally diverse image-question pairs for evaluating retrieval-augmented Visual Question Answering (VQA) across languages and modalities. 

This repository specifically contains the **Wikipedia Retrieval Corpus**, a controlled retrieval environment containing millions of carefully curated multilingual documents relevant to the query domains.

## Dataset Structure

The dataset consists of two configurations:
- `cvqa`: Wikipedia articles relevant to the Culturally-Aware Visual Question Answering domain.
- `worldcuisines`: Wikipedia articles relevant to the food-related visual question answering domain.

## Sample Usage

You can load the retrieval corpus using the Hugging Face `datasets` library:

```python
from datasets import load_dataset

# Load the CVQA Wikipedia retrieval corpus
cvqa_corpus = load_dataset("davidanugraha/M4-RAG", "cvqa", split="train")

# Load the WorldCuisines Wikipedia retrieval corpus
worldcuisines_corpus = load_dataset("davidanugraha/M4-RAG", "worldcuisines", split="train")
```

## Related Datasets
- **CVQA Images**: Available at [`davidanugraha/cvqa`](https://huggingface.co/datasets/davidanugraha/cvqa)
- **WorldCuisines Images**: Available at [`worldcuisines/vqa-v1.1`](https://huggingface.co/datasets/worldcuisines/vqa-v1.1)

## Citation

If you use M4-RAG in your research, please cite:

```bibtex
@article{anugraha2025m4rag,
  title={M4-RAG: A Massive-Scale Multilingual Multi-Cultural Multimodal RAG},
  author={Anugraha, David and Irawan, Patrick Amadeus and Singh, Anshul and Lee, En-Shiun Annie and Winata, Genta Indra},
  journal={arXiv preprint arXiv:2512.05959},
  year={2025},
  url={https://arxiv.org/abs/2512.05959}
}
```